CN112467722B

CN112467722B - Active power distribution network source-network-load-storage coordination planning method considering electric vehicle charging station

Info

Publication number: CN112467722B
Application number: CN202011070004.9A
Authority: CN
Inventors: 郑洁云; 宣菊琴; 张林垚; 陈波; 陈垣玮; 陈晓彬; 倪识远; 黄超; 王震; 胡志坚; 易辰颖
Original assignee: State Grid Fujian Electric Power Co Ltd; Economic and Technological Research Institute of State Grid Fujian Electric Power Co Ltd
Current assignee: State Grid Fujian Electric Power Co Ltd; Economic and Technological Research Institute of State Grid Fujian Electric Power Co Ltd
Priority date: 2020-09-30
Filing date: 2020-09-30
Publication date: 2022-05-13
Anticipated expiration: 2040-09-30
Also published as: CN112467722A

Abstract

The invention relates to an active power distribution network source-network-load-storage coordination planning method considering an electric vehicle charging station, which comprises the steps of firstly carrying out modeling analysis on a distributed power supply, an energy storage device and the electric vehicle charging station which are accessed to a power distribution network; then introducing a coupling relation between the power distribution network and a traffic network by taking the electric vehicle charging station as a junction, converting the traffic flow intercepted by the charging station into traffic economic benefits, and introducing the traffic economic benefits into power distribution network frame planning; taking the optimal benefits of main bodies of a source layer, a network layer and a load layer as a target, making a decision of a planning layer, and taking active management measures such as DG output reduction, on-load tap-changing transformer tap regulation and the like in the optimization of a lower-layer operation simulation layer by taking the minimum expected value of the removal amount of the distributed power supply as a target; and finally, establishing a coordination planning model. The invention realizes the coupling of the electric automobile traffic network and the power distribution network and the overall planning of the power distribution network under the condition of multiple novel element access, and is favorable for good interaction of multiple ends.

Description

Active power distribution network source-network-load-storage coordination planning method considering electric vehicle charging station

Technical Field

The invention relates to the field of power distribution networks, in particular to an active power distribution network source-network-load-storage coordination planning method considering an electric vehicle charging station.

Background

Under the background that clean energy distributed generation, energy storage, novel elements such as electric automobile introduced in a large number in current distribution network, traditional distribution network changes to the intelligent power distribution network of initiative, and the initiative distribution network has the ability of the various access energy of combination control, can carry out active control to the element of access, has improved the utilization ratio of distribution network asset, has strengthened the ability of consuming of distribution network to the energy, is the mainstream trend of current distribution network development. However, the access of the novel elements brings a series of challenges to the traditional power distribution network, the original radial structure of the power distribution network is changed, the influence brought by the access of various novel elements to the power distribution network needs to be comprehensively considered, the multi-end coordination planning of the power supply side, the power grid side and the user side is carried out, and the utilization efficiency of power equipment is improved.

With the great popularization of electric automobiles in China, the influence of large-scale electric automobile load access operation on a power distribution network cannot be ignored, and site selection planning of electric automobile quick charging stations and mutual coupling of a traffic network and a power network need to be considered in planning and operating the power distribution network. The influence of the traffic network on the planning and operation of the power distribution network is considered on the basis of the multi-end coordinated planning of the power distribution network, and the improvement of the economy and the convenience of the traveling of the electric automobile in the planning process is facilitated.

Disclosure of Invention

In view of the above, the invention aims to provide an active power distribution network source-network-load-storage coordination planning method considering an electric vehicle charging station, which considers the mutual influence between a power network and a traffic network under the condition of large-scale electric vehicle load access, cooperatively considers various novel elements of an intelligent power distribution network, performs overall planning from the interests of a power supply, the power network and power users, improves the renewable energy consumption capability of a planning scheme, and improves the coordination of the distribution network planning scheme and the traffic network development under the increasing trend of electric vehicle users.

The invention is realized by adopting the following scheme: an active power distribution network source-network-load-storage coordination planning method considering an electric vehicle charging station comprises the following steps:

step S1: aiming at novel elements including a low-carbon power supply, an electric automobile charging facility and energy storage in an intelligent power distribution network, a Distributed Generation (DG) output time sequence model is established by adopting a time sequence method, an energy storage device charging and discharging model is established from the residual power level and the charging and discharging power, and an energy storage charging and discharging strategy is formulated to reduce equivalent load fluctuation based on the time sequence characteristics of a load and the DG;

step S2: coupling the power distribution network with the traffic network by taking the electric vehicle charging station as a junction, considering the flow distribution of the electric vehicle traffic network, converting the traffic flow intercepted by the charging station into traffic economic benefit and calculating the traffic economic benefit into the economic cost planned by the power distribution network, and determining the configuration capacity of the charging station based on the M/M/S queuing model and the charging waiting time of a user;

step S3: combining planning and operation of the active power distribution network, and constructing a double-layer planning model by using a planning layer as an upper layer and an operation simulation layer as a lower layer; the upper planning layer is divided into a source layer, a network layer and a load layer, the optimal economic benefit of each layer of main body is taken as a target, the power utilization conditions of DGs and energy storage devices are decided respectively according to the location and volume fixing of a grid frame newly-built upgrading condition, the location and volume fixing of an electric vehicle charging station and the participation of a Demand Side Response (DSR) user, active management measures including DG output reduction and on-load tap regulation of a voltage regulating transformer are taken in the lower layer, and the minimum wind curtailment light quantity of a distributed power supply is taken as a target for optimization;

step S4: on the basis of the double-layer planning model established in the step S3, information transfer between the source network and the load three layers and information transfer between the planning layer and the operation simulation layer are realized, and a coordination planning model is established;

step S5: solving the coordination planning model in step S4 by using a modified Particle Swarm Optimization (PSO): on the basis of a standard particle swarm algorithm, a population is subjected to mixed encoding by jointly using a continuous variable and a discrete variable, an individual extreme value and a population extreme value are selected by comparing the advantages and disadvantages of fitness function values in an iteration process, and a method of combining an asynchronous time-varying learning factor and a nonlinear dynamic inertia weight is adopted to solve the problem that the standard particle swarm algorithm is easy to fall into a local solution.

Further, the step S1 specifically includes the following steps:

step S11: the relationship between the wind power output and the wind speed is represented by a piecewise function as follows:

in the formula, V_ci、V_rAnd V_coRespectively the cut-in wind speed, the rated wind speed and the cut-out wind speed of the WTG; p is_r2Rated output power of WTG;

the output power of the photovoltaic generator is related to the illumination intensity by the following formula:

in the formula, P_r1Is rated output power of PVG, I_rIs the rated illumination intensity;

the output of wind power generation and photovoltaic power generation is determined by geographical position and climate environment, the output has obvious time sequence characteristics, and the DG output is described by adopting the time sequence characteristics in different seasons; obtaining wind speed curves and illumination intensity curves in different seasons according to meteorological data, taking the wind speed curves and the illumination intensity curves as input, and obtaining time sequence curves of wind power output and photovoltaic output according to formulas (A1) and (A2), namely a distributed power supply output time sequence model;

step S12: establishing a charge-discharge model of the energy storage device from the aspects of residual power level and charge-discharge power, as follows:

wherein SOC (t) represents the residual capacity level of BESS at time t, and ε represents the hourly loss rate of BESS residual capacity, abbreviated as self-discharge rate, in%/h, P^BESS,c、P^BESS,dis(t) represents the charge and discharge power of BESS, respectively, α and β represent the charge and discharge efficiency of BESS, respectively, E_eLet us the capacity of the BESS, Δ t the sampling interval; load value P of node i at time_Li(t) and DG output value P_DGi(t) the difference is used as the equivalent load,

based on the established energy storage device model, the charging and discharging strategy of the energy storage device is formulated from the angle of stabilizing equivalent load fluctuation as follows:

first, an equivalent load P is defined_eqi(t) and the average equivalent load P_avi：

P_eqi(t)＝P_Li(t)-P_DGi(t) (A4)

In the formula, P_Li(t) and P_DGi(t) is the load value and the DG output value of the node i at the moment respectively;

let Δ P₁For charging power, when P_eqi(t)+ΔP₁＜＜P_aviCharging the battery; when | P_eqi(t)+ΔP₁-P_avi|≤δP_aviCharging the storage battery, wherein delta is a fluctuation coefficient near the average value under the equivalent load; let Δ P₂For discharge power, when P_eqi(t)-ΔP₂＞＞P_aviDischarging the battery; when | P_eqi(t)-ΔP₂-P_avi|≤δP_aviThe battery is discharged.

Further, the specific content of step S2 is:

setting the shortest path always selected by an electric vehicle user as a travel scheme, solving the travel scheme by adopting a Floyd shortest path algorithm, and calculating the traffic flow demand intercepted by the rapid charging station in the region to be planned each year by utilizing a gravity space interaction model:

in the formula (I), the compound is shown in the specification,

the per unit value of the one-way traffic flow demand of the shortest path k in the time period t; w is a_oAnd w_dWeights of a starting point o and an end point d of the path k are respectively used for representing the busy degree of each traffic node; d is a radical of_kIs the per unit value of the k length of the path; s_tAnd s_hRespectively representing the traveling proportion of the electric vehicle user in the time t and the peak time; omega_odThe shortest path set is obtained by using a shortest path model, wherein all nodes of the system are connected in pairs;

the traffic flow intercepted at the time t for the quick charging station at the unit i is obtained;

is a binary variable representing whether the path k passes through the cell i;

establishing a binary variable of whether a quick charging station is established at the unit i;

is a traffic network road set; f_qcIs a traffic flow economic benefit value; omega_fAn economic benefit conversion factor for the intercepted traffic flow;

the average arrival rate of the vehicles to be charged is the proportional allocation of the total frequency demand of the quick charge for the time and the nodes

In the formula, λ_i,tAnd λ_i,hThe average arrival rates of the vehicles to be charged at the unit i, namely the reciprocal of the average arrival time interval of the electric automobile users, are respectively the time t of the quick charging station and the traffic peak time; c^qcThe total frequency requirement for rapid charging of the system;

the charging power of the quick charging station in each time period is determined by the charging time proportion:

in the formula (I), the compound is shown in the specification,

charging power for a unit i at a fast charging station in a time period t; p is a radical of_qcCharging power for a single quick charging device; mu is the average service rate of a single quick charging device, namely the reciprocal of the average time of quick charging;

and simulating the arrival process and service duration of the vehicles to be charged at the quick charging station by using an M/M/S queuing system model in a queuing theory, and setting the most economical quantity of equipment at the quick charging station under the condition that the maximum allowable waiting time is not exceeded.

Further, the step S3 specifically includes the following steps:

step S31: the source layer planning takes the full-cycle income maximization of a DG operator as an optimization target:

in the formula (I), the compound is shown in the specification,

the revenue for the sale of electricity to the DG operator,

is subsidized for the government of new energy power generation,

the benefit is brought for the energy storage,

the cost of the investment of the equipment is low,

the operating cost is DG;

the DG independent operator pursues self maximization under the premise of power grid safety, and the source layer meets the constraints of two aspects of investment and operation; the investment constraints comprise DG investment quantity constraints and energy storage investment quantity constraints which are respectively as follows:

in the formula (I), the compound is shown in the specification,

respectively represents the configuration quantity omega of wind power, photovoltaic and energy storage equipment of the node k_WG、Ω_WG、Ω_DGRepresenting wind, photovoltaic and solar energyA set of candidate nodes with DGs;

the operation constraints comprise energy storage charging and discharging constraints, charge state constraints, power balance constraints of a power distribution network, node voltage safety constraints and line power constraints, which are respectively as follows:

SOC_min≤SOC(t)≤SOC_max (A15)

U_min≤U≤U_max (A17)

P_ij≤P_max (A18)

in the formula, P^ESS,c(t) and P^ESS,dis(t) represents the charging power and the discharging power at time t, respectively, P_maxAnd P_minRespectively representing the maximum and minimum values of charge and discharge power, SOC (t) representing the level of remaining charge at time t, SOC_maxAnd SOC_minRespectively representing maximum and minimum allowable levels of remaining charge, P_iFor node i active injection of power, Q_iReactive power injection is carried out on a node i, j belongs to the set of all nodes directly connected with the node i, and U_iIs the voltage amplitude of node i, G_ijBeing the real part of the nodal admittance matrix, B_ijIs the imaginary part, θ, of the node admittance matrix_ijIs the voltage phase angle difference between node i and node j, U_minIndicating a lower safety limit of voltage, U_maxDenotes the upper voltage safety limit, P_maxRepresents the upper limit of the line power, P_ijRepresents line ij power;

step S32: the network layer planning takes the lowest full-period cost of a power distribution network operator as an optimization target:

in the formula (I), the compound is shown in the specification,

for the purpose of line upgrade and new construction costs,

in order to realize the investment cost of the quick charging station,

in order to obtain the electricity purchasing cost,

in order to increase the cost of the network loss,

in order to regulate and control the cost at the load side,

the value is the economic benefit value of the traffic flow; the constraint conditions to be met by the network layer comprise line upgrading type selection constraint, electric vehicle user maximum charging waiting time constraint, line power constraint and the like, and are respectively as follows:

max{W_t,k}≤W_maxt∈Ω_CF (A21)

in the formula, x_{l_up,i}Is an indication variable of line upgrading and model selection; omega_{l_up0}Represents a collection of lines that are not upgraded; omega_{l_up1}Represents a line set upgraded to line 1; omega_{l_up2}Representing a line set upgraded to line 2, W_t,kRepresenting the user waiting time at time t for charging station k; w_maxIndicating the maximum waiting time, P, of the allowed users_{max_l0}、P_{max_l1}、P_{max_l2}、P_{max_new}Respectively representing the upper limit of allowable power of an original line, an upgrade line type 1, an upgrade line type 2 and a newly-built line;

in addition to the constraints, the network layer planning also needs to meet the network radiation and connectivity constraints, an undirected graph is generated based on a minimum spanning tree algorithm, a directed graph is generated according to a Krusal algorithm, so that network topology is selected, the target network is ensured to be radial, an adjacency matrix and an accessibility matrix of the generated radial network are obtained to judge connectivity, and the connectivity constraint of the network frame is obtained; in addition, the power balance constraint (A16) of the power distribution network and the operation safety constraint (A17) and (A18) mentioned in the source layer planning are also required to be met;

step S33: the maximum satisfaction degree of electricity consumption of DSR users in the cargo layer planning is an objective function:

maxC^H＝λ₁θ+λ₂ε (A23)

in the formula, C^HFor the comprehensive satisfaction of users, theta is the satisfaction of electricity cost, epsilon is the satisfaction of electricity using mode, and lambda₁、λ₂The weight, lambda, of the satisfaction degree of the electricity cost and the satisfaction degree of the electricity using mode₁And λ₂The value of (A) determines the degree of importance of the user to the two satisfaction degrees, and lambda₁+λ₁＝1；

The satisfaction degrees of the electricity cost and the electricity using mode of the user are respectively as follows:

in the formula, C_DSR、C₀Respectively representing the electricity costs, Q, of the users before and after the execution of DSR₀、Q_DSRRespectively representing the total electric quantity of the load before and after the DSR participation; the constraint conditions of the load layer planning are divided from the power balance constraint, the operation safety constraint and the power distribution network connectivity mentioned in the source layer planning and the network layer planningAnd radial constraints, including the constraints of the upper and lower limits of the TOU load shifting-in and shifting-out electric quantity balance and the interruptible load shedding proportion, which are respectively shown as follows:

in the formula, P_s,tThe power consumption is the power consumption in the tth time period before the implementation of the TOU; p_TLO,s,tP_TLI,s,tRespectively carrying out a load transferring-out value and a load transferring-in value in the tth time period of the s quarter after the implementation of the TOU;

respectively carrying out the lower limit and the upper limit of the load transferring proportion in the tth time period of the s quarter after the TOU is implemented;

respectively carrying out the lower limit and the upper limit of the load proportion in the tth time period of the s quarter after the TOU is implemented;

respectively representing the upper limit and the lower limit of the removal of the load of the nth node in the tth period of the s-th quarter;

step S34: the lower layer is an operation simulation layer, and active management measures adopted comprise DG output reduction and transformer tap adjustment; the lower layer takes the minimization of the DG abandoned wind and abandoned light quantity as an operation optimization target:

in the formula (I), the compound is shown in the specification,

represents the total DG subtracted;

respectively representing the active power reduction amount of the kth wind power and photovoltaic equipment at the time t; the lower layer constraint conditions comprise DG output reduction constraint and transformer tap adjusting range constraint:

in the formula, σ_DGIndicating the maximum allowable DG reduction, T_kThe position of the tap of the transformer is indicated,

and

respectively representing the lower and upper limits of the transformer tap adjustment range.

Further, the specific content of step S4 is:

in the upper planning layer, the decision content of each layer of the source network load influences and restricts each other, and in each round of circular optimization, the distributed power supply and energy storage optimization result of the source layer obtained in the step S31 under the current topology and load condition is used

Transmitting to another two layers, making line and charging station decision by combining source layer information and current load condition by network layer, transmitting to load layer, and finally according to step S33, optimizing the result of combining source and network layer in load layer by using optimization model of load layer in step S33, transmitting user power consumption condition to next timeIn circulation; and in the information transmission of the upper layer and the lower layer, a multi-scene technology and an opportunity constraint planning method are adopted to enable the upper layer scene which does not meet the constraint to enter the lower layer, active management measures are adopted, and the optimized scene is transmitted back to the upper layer planning model.

Further, the specific solving process of the coordination planning model in step S5 is as follows:

step S51: data initialization: inputting original data of a power distribution network for planning, and setting current iteration times, maximum iteration times, population size, initial values and final values of learning factors and initial values and final values of inertia weights required by an improved PSO algorithm;

step S52: population initialization: randomly generating an initial population of various decision information about a source, a network and a charge layer, specifically comprising wind power, photovoltaic, energy storage and charging station construction information, line upgrading, new construction information and demand response load reduction proportion information, performing mixed coding on line upgrading and new construction variables as discrete variables and other variables as continuous variables, and randomly generating the initial population;

step S53: obtaining an initial radial network topological structure by adopting a minimum algorithm based on a Kruskal idea;

step S54: carrying out load flow calculation by utilizing Matpower, checking whether opportunity constraint conditions are met, wherein the constraint conditions comprise the power balance constraint, the voltage constraint and the line power constraint of (A16) (A17) (A18), and if the opportunity constraint conditions are met, carrying out the next step; otherwise, starting the lower layer structure;

step S55: calculating the fitness value, namely calculating the objective function value of the source, net and load layer, namely the formula (A11), (A19), (A23) and (A29); setting the fitness to be infinite for scenes which do not meet the constraint condition so as to eliminate the individual in iteration;

step S56: selecting an individual optimal solution and a population optimal solution:

step S57: and iterating, updating the position and the speed of the particle to obtain an updated particle representing the decision information, and updating the learning factor and the inertia weight according to the following formula:

in the formula, ω_iAnd ω_fRespectively an initial value and a final value of the inertia weight omega; c. C_1iAnd c_1f、c_2iAnd c_2fAre respectively a learning factor c₁、c₂Initial and final values of (a); m_kAnd M_maxRespectively the current iteration times and the maximum iteration times;

step S58: revising line parameters, recalculating branch weights, obtaining a new network structure, recalculating the power flow through a MATPOWER tool, namely (A16) - (A18), judging whether opportunity constraint conditions are met, and starting a lower layer model, namely (A29) - (A31) if the opportunity constraint conditions are not met; calculating fitness, and updating an individual optimal solution and a population optimal solution;

step S59: judging whether the iteration is terminated, outputting the optimal scheme and ending if the iteration is terminated, otherwise, repeating the steps S56 to S58 until the iteration is terminated.

Compared with the prior art, the invention has the following beneficial effects:

on the basis of the existing source network load coordination planning, the interaction between the power grid and the traffic network under the condition of large-scale electric automobile charging load access is comprehensively considered, and on the basis, the site selection and volume determination decision content of the electric automobile quick charging station is added in the network frame planning, so that the obtained planning scheme has stronger adaptability; multiple novel elements in the intelligent power distribution network are considered, coordination planning of multiple main bodies is carried out, a power distribution network planning scheme adaptable to the situation of access of the multiple novel elements is obtained, and the renewable energy consumption capacity is improved.

Drawings

Fig. 1 is a schematic diagram of an overall structure of a coordination planning model according to an embodiment of the present invention.

FIG. 2 is a flow chart of model solution according to an embodiment of the present invention.

Fig. 3 is a diagram of an improved IEEE33 node power distribution system topology according to an embodiment of the present invention.

Fig. 4 is a topology diagram of a coupling of a traffic network and a distribution network according to an embodiment of the present invention.

Fig. 5 is a graph comparing DG active power output with electric vehicle charging load according to an embodiment of the present invention.

Fig. 6 is a diagram of changes in business load before and after demand response management according to an embodiment of the present invention, where fig. 6(a) is a diagram of changes in business load before and after demand response management according to a typical day in spring according to an embodiment of the present invention, fig. 6(b) is a diagram of changes in business load before and after demand response management according to a typical day in summer according to an embodiment of the present invention, fig. 6(c) is a diagram of changes in business load before and after demand response management according to a typical day in autumn according to an embodiment of the present invention, and fig. 6(d) is a diagram of changes in business load before and after demand response management according to a typical day in winter according to an embodiment of the present invention.

Fig. 7 is a resident load change diagram before and after demand response management in accordance with the embodiment of the present invention, wherein fig. 7(a) is a resident load change diagram before and after demand response management in a typical day in spring in accordance with the embodiment of the present invention, fig. 7(b) is a resident load change diagram before and after demand response management in a typical day in summer in accordance with the embodiment of the present invention, fig. 7(c) is a resident load change diagram before and after demand response management in a typical day in fall in accordance with the embodiment of the present invention, and fig. 7(d) is a resident load change diagram before and after demand response management in a typical day in winter in accordance with the embodiment of the present invention.

Fig. 8 is an industrial load change diagram before and after demand response management according to an embodiment of the present invention, where fig. 8(a) is an industrial load change diagram before and after demand response management on a typical day in spring according to an embodiment of the present invention, fig. 8(b) is an industrial load change diagram before and after demand response management on a typical day in summer according to an embodiment of the present invention, fig. 8(c) is an industrial load change diagram before and after demand response management on a typical day in autumn according to an embodiment of the present invention, and fig. 8(d) is an industrial load change diagram before and after demand response management on a typical day in winter according to an embodiment of the present invention.

Detailed Description

The invention is further explained below with reference to the drawings and the embodiments.

It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular is intended to include the plural unless the context clearly dictates otherwise, and it should be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of features, steps, operations, devices, components, and/or combinations thereof.

As shown in fig. 1 and 2, the present embodiment provides an active power distribution network source-network-load-storage coordination planning method considering an electric vehicle charging station, including the following steps:

step S3: combining planning and operation of the active power distribution network, and constructing a double-layer planning model by using a planning layer as an upper layer and an operation simulation layer as a lower layer; the upper planning layer is divided into a source layer, a network layer and a load layer, the optimal economic benefit of each layer of main body is taken as a target, the power utilization conditions of DGs and energy storage devices are decided respectively according to the location and volume fixing of a grid frame, the new building and upgrading conditions of the grid frame, the location and volume fixing of an electric vehicle charging station and the participation of DSR (demand side response) users, active management measures including DG output reduction and on-load tap regulation of a voltage regulating transformer are taken in the lower layer, and the minimum wind curtailment light quantity of a distributed power supply is taken as a target for optimization;

step S5: solving the coordination planning model in step S4 by using a modified Particle Swarm Optimization (PSO): on the basis of a standard particle swarm algorithm, a mixed encoding is carried out on a swarm by jointly using a continuous variable and a discrete variable, an individual extreme value and a swarm extreme value are selected by comparing the quality of a fitness function value in an iteration process, and a method of combining an asynchronous time-varying learning factor and a nonlinear dynamic inertia weight is adopted to solve the problem that the standard particle swarm algorithm is easy to sink into a local solution.

In this embodiment, the step S1 specifically includes the following steps:

step S12: establishing a charge-discharge model of the energy storage device from the residual power level and the charge-discharge power, as follows:

wherein SOC (t) represents the residual capacity level of BESS at time t, and ε represents the hourly loss rate of BESS residual capacity, abbreviated as self-discharge rate, in%/h, P^BESS,c、P^BESS,dis(t) represents the charge and discharge power of BESS, respectively, α and β represent the charge and discharge efficiency of BESS, respectively, E_eLet us the capacity of the BESS, Δ t the sampling interval; load value P of node i at time_Li(t) and DG output value P_DGiThe difference in (t) is used as the equivalent load,

P_eqi(t)＝P_Li(t)-P_DGi(t) (A4)

In this embodiment, the specific content of step S2 is:

and then, coupling the power distribution network with the traffic network by taking the electric vehicle charging station as a junction, and considering the flow distribution of the electric vehicle traffic network. Setting an electric vehicle user to always select a shortest path as a travel scheme, adopting a Floyd shortest path algorithm to obtain the travel scheme, and calculating the traffic flow demand intercepted by a rapid charging station in a region to be planned each year by utilizing a gravity space interaction model:

in the formula (I), the compound is shown in the specification,

the per unit value of the one-way traffic flow demand of the shortest path k in the time period t; w is a_oAnd w_dWeights of a starting point o and an end point d of the path k are respectively used for representing the busy degree of each traffic node; d_kIs the per unit value of the k length of the path; s is_tAnd s_hRespectively representing the traveling proportion of the electric vehicle user in the time t and the peak time; omega_odThe shortest path set is obtained by using a shortest path model, wherein all nodes of the system are connected in pairs;

is a binary variable representing whether the path k passes through the cell i;

In the formula, λ_i,tAnd λ_i,hThe average arrival rates of the vehicles to be charged at the time t of the rapid charging station at the unit i and the traffic peak time are respectively the reciprocal of the average arrival time interval of the electric vehicle users; c^qcThe total frequency requirement for rapid charging of the system;

in the formula (I), the compound is shown in the specification,

In this embodiment, the following steps are performed to model the source, the network, the load layers and the operation simulation layer, respectively, and include the following steps:

in the formula (I), the compound is shown in the specification,

the revenue for the sale of electricity to the DG operator,

is subsidized for the government of new energy power generation,

in order to realize the benefit of energy storage,

the cost of the investment of the equipment is low,

the operating cost is DG;

in the formula (I), the compound is shown in the specification,

respectively represents the configuration quantity omega of wind power, photovoltaic and energy storage equipment of the node k_WG、Ω_WG、Ω_DGRepresenting wind power, photovoltaic and a set of all DG alternative nodes;

SOC_min≤SOC(t)≤SOC_max (A15)

U_min≤U≤U_max (A17)

P_ij≤P_max (A18)

in the formula, P^ESS,c(t) and P^ESS,dis(t) represents the charging power and the discharging power at time t, respectively, P_maxAnd P_minRespectively representing the maximum and minimum values of charge and discharge power, SOC (t) representing the level of remaining charge at time t, SOC_maxAnd SOC_minRespectively representing maximum and minimum allowable levels of remaining charge, P_iFor active injection of power, Q, into node i_iReactive power injection is carried out on a node i, j belongs to the set of all nodes directly connected with the node i, and U_iIs the voltage amplitude of node i, G_ijBeing the real part of the nodal admittance matrix, B_ijFor the imaginary part, theta, of the node admittance matrix_ijIs the voltage phase angle difference between node i and node j, U_minIndicating a lower safety limit of voltage, U_maxDenotes the upper voltage safety limit, P_maxRepresents the upper limit of the line power, P_ijRepresents line ij power;

in the formula (I), the compound is shown in the specification,

for the purpose of line upgrade and new construction costs,

in order to realize the investment cost of the quick charging station,

in order to obtain the electricity purchasing cost,

in order to increase the cost of the network loss,

the cost is regulated and controlled for the load side,

is a traffic flow economic benefit value; the constraint conditions to be met by the network layer comprise line upgrading type selection constraint, electric vehicle user maximum charging waiting time constraint, line power constraint and the like, and are respectively as follows:

max{W_t,k}≤W_maxt∈Ω_CF (A21)

in the formula, x_{l_up,i}Is an indication variable of line upgrading selection; omega_{l_up0}Represents a collection of lines that are not upgraded; omega_{l_up1}Representative upgrade to lineA line set of type 1; omega_{l_up2}Representing a line set upgraded to line 2, W_t,kRepresenting the user waiting time at time t for charging station k; w_maxIndicating the maximum waiting time, P, of the allowed users_{max_l0}、P_{max_l1}、P_{max_l2}、P_{max_new}Respectively representing the upper limit of allowable power of an original line, an upgrade line type 1, an upgrade line type 2 and a newly-built line;

in addition to the constraints, the network layer planning also needs to meet network radial and connectivity constraints, an undirected graph is generated based on a minimum spanning tree algorithm, a directed graph is generated according to a Kruscal algorithm, network topology is selected according to the undirected graph, the target network is ensured to be radial, an adjacency matrix and an accessibility matrix of the generated radial network are obtained, and connectivity is judged, and the adjacency matrix and the accessibility matrix are used as the connectivity constraints of the network frame; in addition, the power balance constraint (A16) of the power distribution network and the operation safety constraint (A17) and (A18) mentioned in the source layer planning are also required to be met;

step S33: the maximum satisfaction degree of electricity utilization of DSR users in the floor-load planning is an objective function:

maxC^H＝λ₁θ+λ₂ε (A23)

in the formula, C^HFor the comprehensive satisfaction of users, theta is the satisfaction of electricity cost, epsilon is the satisfaction of electricity using mode, and lambda₁、λ₂The weight, lambda, of the satisfaction degree of the electricity cost and the satisfaction degree of the electricity using mode₁And λ₂The value of (A) determines the attention degree of the user to two satisfaction degrees, the two satisfaction degrees can be subjectively assigned according to the market research result, and the lambda value is₁+λ₁＝1；

in the formula, C_DSR、C₀Respectively representing the electricity costs, Q, of the users before and after the execution of DSR₀、Q_DSRRespectively representing the total electric quantity of the load before and after the DSR participation; the constraint conditions of the load layer planning include, in addition to the power balance constraint, the operation safety constraint, the power distribution network connectivity and the radial constraint mentioned in the source layer and the network layer planning, the upper and lower limits constraints of the TOU load transfer-in/out electric quantity balance and the interruptible load shedding proportion, which are respectively as follows:

in the formula, P_s,tThe power consumption is the power consumption in the tth time period before the implementation of the TOU; p_TLO,s,tP_TLI,s,tRespectively transferring out a load value and a load value in the tth time period of the s quarter after the implementation of the TOU;

in the formula (I), the compound is shown in the specification,

represents the total DG cut;

respectively representing the active power reduction amount of the kth wind power and photovoltaic equipment at the time t; the constraints of the lower layers include DG output reduction constraint and transformer tap adjustment range constraint:

and

In this embodiment, the specific content of step S4 is:

in the upper planning layer, the decision content of each layer of the source network load all affect and restrict each other, and in each round of circular optimization, the distributed power supply and the distributed power supply of the source layer obtained in the step S31 under the current topology and load condition are combinedEnergy storage optimization results

Transmitting the information to the other two layers, performing decision of a line and a charging station by combining the information of the source layer and the current load condition through the network layer, transmitting the decision to the load layer, and finally optimizing the result of combining the source layer and the network layer in the load layer according to the step S33, namely optimizing by adopting the optimization model of the load layer in the step S33, and transmitting the electricity utilization condition of the user to the next cycle; and adopting a multi-scene technology and an opportunity constraint planning method in the information transmission of the upper layer and the lower layer, entering the upper layer scene which does not meet the constraint into the lower layer, taking active management measures, and transmitting the optimized scene back to the upper layer planning model. The method comprises the following steps that a multi-scene technology and an opportunity constraint planning method are adopted in information transmission of an upper layer and a lower layer, scenes which do not meet constraints on the upper layer enter the lower layer, active management measures are adopted, and the optimized scenes are transmitted back to an upper layer planning model; namely presetting a reasonable confidence coefficient (such as 80%); the method comprises the steps of obtaining scene probability meeting constraint through simulation of operation of multiple scenes, judging that the planning scheme can be adopted only when the probability is greater than confidence, and abandoning the planning scheme otherwise, so that the result is reliable to a certain extent and not too severe, and negative effects of small-probability events on the decision-making scheme are avoided.

In this embodiment, the specific solving process of the coordinated planning model in step S5 is as follows:

step S55: calculating the fitness value, namely calculating the objective function value of the source, net and load layer, namely the formula (A11), (A19), (A23) and (A29); setting the fitness to infinity for scenes not meeting the constraint condition to eliminate the individual in iteration;

in the formula, ω_iAnd ω_fRespectively an initial value and a final value of the inertia weight omega; c. C_1iAnd c_1f、c_2iAnd c_2fAre respectively a learning factor c₁、c₂The initial value and the final value of (c); m_kAnd M_maxRespectively the current iteration times and the maximum iteration times;

step S58: revising line parameters, recalculating branch weights, obtaining a new network structure, recalculating the load flow through a MATPOWER tool, namely (A16) - (A18), judging whether opportunity constraint conditions are met, and starting a lower layer model, namely (A29) - (A31) if the opportunity constraint conditions are not met; calculating fitness, and updating an individual optimal solution and a population optimal solution;

Preferably, in the present embodiment, the relationship between the wind power output and the wind speed can be represented by a piecewise function as follows:

in the formula, V_ci、V_rAnd V_coRespectively the cut-in wind speed, the rated wind speed and the cut-out wind speed of the WTG; p_r2Is the rated output power of the WTG.

The output power of the photovoltaic generator can be expressed in relation to the illumination intensity by the following formula:

in the formula, P_r1Is rated output power of PVG, I_rThe rated illumination intensity.

The output of wind power generation and photovoltaic power generation is mainly determined by geographical positions and climatic environments, the output has obvious time sequence characteristics, and DG output is described by adopting the time sequence characteristics in different seasons. And obtaining wind speed curves and illumination intensity curves in different seasons according to meteorological data, taking the wind speed curves and the illumination intensity curves as input, and obtaining time sequence curves of wind power output and photovoltaic output according to formulas (A1) and (A2).

Establishing a charge-discharge model of the energy storage device from the residual power level and the charge-discharge power, as follows:

wherein SOC (t) represents the residual capacity level of BESS at time t, and ε represents the hourly loss rate of BESS residual capacity, abbreviated as self-discharge rate, in%/h, P^BESS,c、P^BESS,dis(t) represents the charge and discharge power of BESS, respectively, α and β represent the charge and discharge efficiency of BESS, respectively, E_eΔ t is the sampling interval for the capacity of BESS.

Load value P of node i at time_Li(t) and DG output value P_DGi(t) the difference is used as the equivalent load,

based on the established energy storage device model, a charging and discharging strategy of the energy storage device is formulated from the perspective of stabilizing equivalent load fluctuation as follows. First, defining an equivalent load P_eqi(t) and the average equivalent load P_avi：

P_eqi(t)＝P_Li(t)-P_DGi(t) (4)

In the formula, P_Li(t) and P_DGiAnd (t) are the load value and the DG output value of the node i at the moment respectively.

Let Δ P₁For charging power, when P_eqi(t)+ΔP₁＜＜P_aviCharging the battery; when | P_eqi(t)+ΔP₁-P_avi|≤δP_aviAnd charging the storage battery, wherein delta is a fluctuation coefficient around the average value under the equivalent load.

Let Δ P₂For discharge power, when P_eqi(t)-ΔP₂＞＞P_aviThe battery discharges; when | P_eqi(t)-ΔP₂-P_avi|≤δP_aviThe battery is discharged.

And then, coupling the power distribution network with the traffic network by taking the electric vehicle charging station as a hub, and considering the flow distribution of the electric vehicle traffic network. Setting an electric vehicle user to always select a shortest path as a travel scheme, solving a storage scheme by using a Floyd shortest path algorithm, and calculating traffic flow requirements intercepted by a full-system quick charging station every year by using a gravity space interaction model:

in the formula (I), the compound is shown in the specification,

the per unit value of the one-way traffic flow demand of the shortest path k in the time period t; w is a_oAnd w_dWeights of a starting point o and an end point d of the path k are respectively used for representing the busy degree of each traffic node; d_kIs the per unit value of the k length of the path; s_tAnd s_hRespectively representing the traveling proportion of the electric vehicle user in the time t and the peak time; omega_odThe shortest path set is obtained by using a shortest path model, wherein all nodes of the system are connected in pairs;

is a binary variable representing whether the path k passes through the cell i;

is a traffic network road set; f_qcIs a traffic flow economic benefit value; omega_fThe economic benefit conversion coefficient of the intercepted traffic flow is obtained.

In the formula, λ_i,tAnd λ_i,hThe average arrival rates of the vehicles to be charged at the unit i, namely the reciprocal of the average arrival time interval of the electric automobile users, are respectively the time t of the quick charging station and the traffic peak time; c^qcThe total frequency requirement for rapid charging of the system.

in the formula (I), the compound is shown in the specification,

charging power for a unit i at a fast charging station in a time period t; p is a radical of formula_qcCharging power for a single quick charging device; mu is the average service rate of a single quick-charging device, namely the reciprocal of the average time of quick charging.

Modeling is performed on the source layer, the net layer, the load layer and the operation simulation layer respectively.

The source layer planning takes the full-cycle income maximization of a DG operator as an optimization target:

(1) DG operator revenue for selling electricity

(2) Government subsidy for new energy power generation

(3) Low-storage high-emission profit margin

(4) Investment cost of equipment

(5) DG operating cost

In the formula, C^YIs the annual integrated value of the DG operator revenue; omega_WG、Ω_PVRespectively representing a set of nodes allowing installation of wind power and photovoltaic; omega_DGRepresenting the total set of DG mounting positions, i.e. omega_DG＝Ω_WG∪Ω_PV；Ω_tIs a scene set;

representing the yield of DG unit power generation;

representing the government subsidy income of DG unit power generation;

representing the operating cost of the DG for sending out unit electric quantity; beta is an equal annual value coefficient; r is the discount rate; t is the service life of the equipment;

respectively representing the investment costs of wind power, photovoltaic and energy storage equipment;

respectively representing the investment cost of a single wind power device, a single photovoltaic device and a single energy storage device;

respectively representing the configuration quantity of wind power, photovoltaic and energy storage equipment of the node k;

representing the DG total output force at the k node at the time t; n is a radical of_sRepresenting the total number of scenes;

and

respectively the charging and discharging power of photovoltaic and BESS at the fan node i at the time t, c_z,rtIs the time-of-use electricity price at the time t,

for the charging and discharging state of the BESS at the photovoltaic node i at time t,

indicating that the BESS is in a charging state,

indicating that the BESS is in a discharge state,

the charge-discharge state of BESS at the fan node i at the time t is defined as

The same is true.

The DG independent operator should pursue self maximization under the premise of power grid safety, and the source layer should satisfy the constraints of two major aspects of investment and operation. The investment constraints comprise DG investment quantity constraints and energy storage investment quantity constraints which are respectively as follows:

in the formula (I), the compound is shown in the specification,

respectively represents the configuration quantity omega of wind power, photovoltaic and energy storage equipment of the node k_WG、Ω_WG、Ω_DGRepresenting wind power, photovoltaic and the set of all DG candidate nodes.

SOC_min≤SOC(t)≤SOC_max (22)

U_min≤U≤U_max (24)

P_ij≤P_max (25)

in the formula, P^ESS,c(t) and P^ESS,dis(t) represents the charging power and the discharging power at time t, respectively, P_maxAnd P_minRespectively representing the maximum and minimum values of charge and discharge power, SOC (t) representing the level of remaining charge at time t, SOC_maxAnd SOC_minRespectively representing maximum and minimum allowable levels of remaining charge, P_iFor active injection of power, Q, into node i_iReactive power injection is carried out on a node i, j belongs to the set of all nodes directly connected with the node i, and U_iIs the voltage amplitude of node i, G_ijAs the real part of the nodal admittance matrix, B_ijFor the imaginary part, theta, of the node admittance matrix_ijIs the voltage phase angle difference between node i and node j, U_minIndicating a lower safety limit of voltage, U_maxDenotes the upper voltage safety limit, P_maxRepresents the upper limit of the line power, P_ijRepresenting line ij power.

The network layer planning takes the lowest full-period cost of a power distribution network operator as an optimization target:

(1) line upgrade and new construction costs

In the formula (I), the compound is shown in the specification,

representing the upgrade cost of the line;

showing the construction cost of the newly-built line;

an equal-year-value coefficient representing line investment;

represents the upgrade cost per unit length of the line type v; c^l_newRepresenting the new construction cost of a unit length line; l. the_kRepresents the length of the kth branch; omega_{l_up}Representing a set of upgraded lines; omega_{l_new}Representing a set of newly created lines.

(2) Investment cost of quick charging station

In the formula (I), the compound is shown in the specification,

an equal-year-value coefficient representing the investment of the rapid charging station;

the cost of a single quick charging device is represented;

a variable 0-1 representing the station building of the charging station, wherein 1 represents the station building, and 0 represents the station non-building;

the number of the rapid charging equipment configured in the kth rapid charging station is represented; omega_CFAnd representing a candidate node set of the rapid charging station.

(3) Cost of electricity purchase

In the formula (I), the compound is shown in the specification,

represents a fee for purchasing electricity to the DG independent operator;

representing the cost of purchasing electricity from the superior power grid; c_eRepresenting the electricity purchase charge per unit electricity quantity; omega_busRepresenting a collection of distribution network nodes.

(4) Cost of loss of network

In the formula,. DELTA.P_k,tRepresenting the active power loss of the kth branch at the moment t; omega_lineRepresenting a collection of distribution network lines.

(5) Load side regulation cost

In the formula, C_{DR_s}Indicating reduced electricity sales costs for implementing Demand Side Response (DSR); c_{DR_b}Represents a compensation fee for interruptible load;

indicating a power sell fee for not implementing DSR;

representing electricity sales fees after DSR implementation; c_bRepresenting the compensation cost of the unit of the interrupted electric quantity;

representing the interrupt load power of the k node at time t.

(6) Economic benefit value of traffic flow

In the formula (I), the compound is shown in the specification,

is a binary variable representing whether the path k passes through the cell i;

The constraint conditions to be met by the network layer comprise line upgrading type selection constraint, electric vehicle user maximum charging waiting time constraint, line power constraint and the like, and are respectively as follows:

max{W_t,k}≤W_maxt∈Ω_CF (42)

in the formula, x_{l_up,i}Is an indication variable of line upgrading and model selection; omega_{l_up0}Represents a collection of lines that are not upgraded; omega_{l_up1}Represents a line set upgraded to line 1; omega_{l_up2}Representing a line set upgraded to line 2, W_t,kRepresenting the user waiting time at time t for charging station k; w_maxIndicating the maximum waiting time, P, of the allowed users_{max_l0}、P_{max_l1}、P_{max_l2}、P_{max_new}Respectively representing the upper limit of the allowable power of the original line, the upgrade line type 1, the upgrade line type 2 and the newly-built line.

In addition to the above constraints, the network layer planning also needs to meet the network radial and connectivity constraints, an undirected graph is generated based on a minimum spanning tree algorithm, a directed graph is generated according to a Krusal algorithm, so as to select a network topology, ensure that a target network is radial, and determine connectivity by solving an adjacency matrix and a reachability matrix of the generated radial network, so as to serve as the connectivity constraint of the network frame. In addition, the power balance constraint and the operation safety constraint of the power distribution network mentioned in the source layer planning are required to be met.

maxC^H＝λ₁θ+λ₂ε (44)

in the formula, C^HFor the comprehensive satisfaction of users, theta is the satisfaction of electricity cost, epsilon is the satisfaction of electricity using mode, and lambda₁、λ₂The weight, lambda, of the satisfaction degree of the electricity cost and the satisfaction degree of the electricity using mode₁And λ₂The value of (A) determines the attention degree of the user to two satisfaction degrees, the two satisfaction degrees can be subjectively assigned according to the market research result, and the lambda value is₁+λ₁＝1。

(1) satisfaction degree of electricity consumption cost:

the power consumption cost satisfaction is an index for measuring the amount of change in the power consumption cost of the user before and after execution of the DSR project. It is used for expressing the change degree of the user's electric charge payment from the user's interest perspective.

In the formula, theta is the satisfaction degree of electricity consumption cost; c_DSR，C₀The electricity costs of the users before and after the DSR is executed are shown.

Therefore, the satisfaction degree of the electricity cost of the user can be represented by the following formula:

in the formula, C_cThe sum of the adjustment compensation costs of the interruptible load nodes in all scenes; c_bA sum of saved electricity purchase costs for interruptible load nodes under all scenarios; m is_cIs the compensation price of unit electricity quantity; alpha is alpha_n,s,tRepresenting the interruption proportion of the interruptible load in the tth time period of the s quarter; p_L,n,s,tRepresenting the load value of the nth node in the s-th quarter and the t-th period before the demand response; p_tElectricity for time period tA price; n is the total number of nodes participating in the interruptible load response item.

(2) Satisfaction degree of electricity utilization mode

After the DSR strategy is implemented, the load end responds to the DSR signal, specifically, the electricity utilization mode is rearranged, and a new electricity utilization load curve is formed. The power mode satisfaction measures the degree of change of the new load curve relative to the original load curve after responding to the DSR event, as shown in the following equation:

wherein epsilon is the satisfaction degree of the power utilization mode, and the closer epsilon is to 1, the less the change of the power utilization mode of the user is, the higher the satisfaction degree of the power utilization mode is. Q₀、Q_DSRRespectively representing the total electric quantity of the load before and after the participation of the DSR.

Thus, the available user satisfaction with electricity expression is as follows:

in the formula, P_L,n,s,tIndicating the original electricity usage of the nth node during the tth time period in the s quarter before DSR is performed.

The constraint conditions of the load layer planning include, in addition to the power balance constraint, the operation safety constraint, the power distribution network connectivity and the radial constraint mentioned in the source layer and the network layer planning, the upper and lower limits constraints of the TOU load transfer-in/out electric quantity balance and the interruptible load shedding proportion, which are respectively as follows:

respectively representing the upper limit and the lower limit of the load of the nth node in the tth period of the s-th quarter.

Step S34: the lower layer is an operation simulation layer, and active management measures adopted comprise DG output reduction and transformer tap adjustment. The lower layer takes the minimized wind and light abandonment amount as an operation optimization target:

in the formula (I), the compound is shown in the specification,

represents the total DG cut;

and respectively representing the active power reduction amount of the kth wind power and photovoltaic equipment at the moment t.

The lower layer constraint conditions comprise DG output reduction constraint and transformer tap adjustment range constraint:

and

In the upper planning layer, decision contents of source network charge layers all influence and restrict each other, in each round of circulation optimization, DG and energy storage location capacity results of a source layer under the conditions of current topology and load are transmitted to the other two layers, then line and charging station decisions are carried out by combining information of the source layer and the current load conditions of the network layer and are transmitted to the charge layers, finally the charge layers are optimized by combining the results of the source layer and the network layer, and the power utilization condition of a user is transmitted to the next circulation.

A multi-scene technology and an opportunity constraint planning method are adopted in the information transmission of the upper layer and the lower layer (namely, a reasonable confidence coefficient (such as 80%) is preset, the probability of a scene meeting the constraint is obtained through simulating the operation of the multi-scene, the planning scheme can be adopted only when the probability is judged to be greater than the confidence coefficient, otherwise, the planning scheme is abandoned, the result can be ensured to be reliable to a certain degree and not too severe, the negative influence of a small probability event on a decision-making scheme is avoided), the scene which does not meet the constraint at the upper layer enters a lower layer active management model, and the optimized scene is transmitted back to the upper layer planning model.

The specific solving process of the coordination planning model is as follows:

1) and (6) initializing data. Inputting original data of a power distribution network for planning, and setting current iteration times, maximum iteration times, population size, initial values and final values of learning factors and initial values and final values of inertia weights required by the improved PSO algorithm.

2) And (5) initializing a population. The method comprises the steps of randomly generating an initial population of various decision-making information about a source, a network and a charge layer, specifically comprising wind power, photovoltaic, energy storage and charging station construction information, line upgrading, new construction information and demand response load reduction proportion information, performing mixed coding on line upgrading and new construction variables as discrete variables and other variables as continuous variables, and randomly generating the initial population.

3) And obtaining an initial radial network topological structure by adopting a minimum algorithm based on a Kruskal idea.

4) Carrying out load flow calculation by utilizing Matpower, checking whether opportunity constraint conditions are met, wherein the constraint conditions comprise the power balance constraint, the voltage constraint and the line power constraint in the steps (23), (24) and (25), and if the opportunity constraint conditions are met, carrying out the next step; otherwise, the lower layer structure is started.

5) Calculating the fitness value, namely calculating the objective function value of the source, net and load layer, namely the equations (11), (26), (44) and (54); and setting the fitness to infinity for the scenes which do not meet the constraint condition so as to eliminate the individual in iteration.

6) And selecting an individual optimal solution and a population optimal solution.

7) And iterating, updating the position and the speed of the particle to obtain an updated particle representing the decision information, and updating the learning factor and the inertia weight according to the following formula:

in the formula, ω_iAnd ω_fRespectively an initial value and a final value of the inertia weight omega; c. C_1iAnd c_1f、c_2iAnd c_2fAre respectively a learning factor c₁ c₂Initial and final values of (c); m_kAnd M_maxRespectively the current iteration number and the maximum iteration number.

8) Revising the line parameters, recalculating the branch weight to obtain a new network structure, then recalculating the load flow, judging whether the opportunity constraint condition is met, and starting the lower model if the opportunity constraint condition is not met. And calculating the fitness, and updating the individual optimal solution and the population optimal solution.

9) Judging whether the iteration is terminated, outputting the optimal scheme and finishing if the iteration is terminated, otherwise, repeating 6) to 8) until the iteration is terminated. The following is illustrated by specific examples:

the simulation system of the invention adopts an improved 33-node system, and the topological diagram of the system is shown in figure 3.

The improved 33-node system shown in fig. 3 includes 33 original nodes, which are nodes 1 to 33, and 6 newly added load nodes, which are nodes 36 to 39; the system comprises 37 original branches which are branches (1) to (37), and 24 branches to be newly built which are branches (38) to (61). Considering that the types of the connected DGs are wind driven generators and photovoltaic generators, the capacity of each unit is 0.1MW, and the allowed maximum permeability is 50%. The alternative nodes of the wind and photovoltaic generators are 3, 6, 16, 27 and 8, 10, 28, 30 respectively, and the upper limit of the number of installations is 20, 30, 20. The alternative node of the energy storage device coincides with the DG alternative node. In this example, the

nodes

4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, and 37 are assumed to be the residential load nodes;

nodes

2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35, 38 are commercial load nodes;

nodes

3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, 36, 39 are industrial load nodes. A demand-side response strategy based on time-of-use electricity prices is implemented for business and industrial loads, and an incentive demand response strategy is implemented for residential and commercial loads, so that the loads can be interrupted. The line operation and maintenance rate and the discount rate are respectively set to be 3% and 0.1, the fixed investment recovery period of the line is 20 years, and the fixed investment recovery period of the DG and the BESS is 10 years. The relevant parameters of the line, DG and BESS are shown in table 1, table 2 and table 3, respectively.

TABLE 1 line parameters

TABLE 2 DG parameters

TABLE 3 BESS parameters

The original nodes 1-33 in the system are used as alternative nodes of the electric automobile quick charging station, an electric automobile traffic network diagram is simulated according to geographic information and is coupled with a power line, and the obtained topological diagram is shown in fig. 4. In fig. 4, numerals in parentheses indicate distances between nodes. In the present embodiment, the efficiency of the transformer and the efficiency of the charger used by the charging station are respectively 95% and 90%, the charging power of a single charging device is 60kW, the charging amount of each electric vehicle is 30kWh, and the upper limit of the site selection number of the electric vehicle charging station is 8. The node traffic flow weight, the trip proportion of the electric vehicle in each time period, and the relevant information of the regional electric vehicle are shown in tables 4, 5, and 6, respectively.

TABLE 4 node traffic flow weights

TABLE 5 electric automobile trip proportion in each time period

TABLE 6 regional electric vehicle-related information

The improved PSO algorithm parameters are set as follows: the maximum cycle time is 50 times, the iteration times of the source layer and the net layer are 20 times, the population size is 100, the iteration times of the net layer is 15 times, and the population size is 80. The initial value and the final value of the inertia weight are respectively 0.8 and 0.4, the initial value and the final value of one learning factor are respectively 2.5 and 0.5, and the initial value and the final value of the other learning factor are respectively 0.5 and 2.5.

Solving and simulating the model to obtain the source layer with the profit of-443.6460 ten thousand yuan, namely the economic cost of 443.6460 ten thousand yuan; the economic cost of the net layer is 3174.9942 ten thousand yuan; the satisfaction index of the charged layer electricity consumer is 0.9641.

TABLE 4 Source layer planning results

TABLE 5 net layer net rack planning result

Table 6 network layer electric vehicle charging station planning result

According to the charging station planning result, after the traffic flow captured by the electric vehicle charging station is converted into the economic benefit value, the charging station site selection result comprises points with higher node vehicle flow weight, such as

nodes

3, 5, 20 and 27, wherein the node 20 is located at the core position of intersection of a plurality of roads in the view of traffic network layout, so that the purpose of intercepting more traffic network flow is achieved in the charging station planning process of the distribution network, and the charging requirement of electric vehicle users in the traffic network is met. Meanwhile, as can be seen from the source layer planning result, the addresses of some electric vehicle charging stations are consistent with the DG addresses, that is, the node 3 and the node 27. Taking node 3 as an example, the timing curve of charging load and DG output of the electric vehicle is shown in fig. 5. It can be seen from the figure that the electric vehicle charging load of the node 3 can consume part of the DG output, and the coordination of the charging station site selection and the DG site selection is beneficial to realizing the on-site consumption of renewable energy, improving the utilization rate of the renewable energy, and reducing the phenomena of wind abandonment and light abandonment.

In the planning result of the load layer, the user satisfaction results fluctuate near 0.96 in the multiple iteration process, fig. 6 to 8 are comparison graphs of comparison load curves before and after the DSR is implemented by commercial load users, residential load users and industrial load users respectively, and the adopted demand side response strategy combining price type and incentive type can realize better peak clipping and valley filling effects under the condition of ensuring the satisfaction of the electricity users.

The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.

Claims

1. An active power distribution network source-network-load-storage coordination planning method considering an electric vehicle charging station is characterized by comprising the following steps: the method comprises the following steps:

step S1: aiming at novel elements including a low-carbon power supply, an electric automobile charging facility and energy storage in an intelligent power distribution network, a time sequence method is adopted to establish a distributed power supply output time sequence model, an energy storage device charging and discharging model is established from the residual power level and the charging and discharging power, and an energy storage charging and discharging strategy is formulated to reduce equivalent load fluctuation based on the load and the time sequence characteristics of the distributed power supply;

step S3: combining planning and operation of the active power distribution network, and constructing a double-layer planning model by using a planning layer as an upper layer and an operation simulation layer as a lower layer; the upper layer is divided into a source layer, a net layer and a load layer, with the optimal economic benefit of main bodies of all layers as a target, decision is made on the DG and the location and capacity of an energy storage device, the newly-built upgrading condition of a net rack, the location and capacity of an electric vehicle charging station and the power utilization condition of a user participating in demand side response respectively, active management measures including DG output reduction and on-load tap-changing transformer tap regulation are adopted in the lower layer, and optimization is carried out with the minimum wind curtailment quantity of a distributed power supply as a target;

step S5: solving the coordination planning model in the step S4 by adopting an improved particle swarm optimization: on the basis of a standard particle swarm algorithm, a population is subjected to mixed encoding by jointly using a continuous variable and a discrete variable, an individual extreme value and a population extreme value are selected by comparing the advantages and disadvantages of fitness function values in an iteration process, and a method of combining an asynchronous time-varying learning factor and a nonlinear dynamic inertia weight is adopted to solve the problem that the standard particle swarm algorithm is easy to fall into a local solution;

the step S3 specifically includes the following steps:

(A11)

in the formula (I), the compound is shown in the specification,

the revenue for the sale of electricity to the DG operator,

is subsidized for the government of new energy power generation,

in order to realize the benefit of energy storage,

the cost of the investment of the equipment is low,

the operating cost is DG;

(A12)

(A13)

in the formula (I), the compound is shown in the specification,

、

、

respectively representing nodes

The configuration quantity of wind power, photovoltaic and energy storage equipment,

、

、

respectively representing nodes

The maximum value of the configuration quantity of wind power, photovoltaic and energy storage equipment,

、

、

representing wind power, photovoltaic and a set of all DG alternative nodes;

(A14)

(A15)

(A16)

(A17)

(A18)

in the formula (I), the compound is shown in the specification,

and

respectively represent

The charging power and the discharging power at the moment,

and

respectively represent the maximum value and the minimum value of the charge and discharge power,

to represent

The level of the remaining power at the moment,

and

respectively representing maximum and minimum remaining capacity allowable levels,

there is an active injection of power for node i,

the power is reactive injected for node i,

for all and nodes

Set of directly connected nodes, U_iIs the voltage amplitude of node i, G_ijBeing the real part of the nodal admittance matrix, B_ijFor the imaginary part of the node admittance matrix,

is node i and node

The difference in the voltage phase angles of (c),

the lower limit of the voltage safety is shown,

the safe upper limit of the voltage is shown,

represents the upper limit of the line power, P_ijIndicating line

Power;

(A19)

in the formula (I), the compound is shown in the specification,

for the purpose of line upgrade and new construction costs,

in order to realize the investment cost of the quick charging station,

in order to obtain the electricity purchasing cost,

in order to increase the cost of the network loss,

in order to control the cost at the load side,

is a traffic flow economic benefit value; the constraint conditions to be met by the network layer comprise a line upgrading type selection constraint, an electric vehicle user maximum charging waiting time constraint and a line power constraint, which are respectively as follows:

(A20)

(A21)

(A22)

in the formula (I), the compound is shown in the specification,

is an indication variable of line upgrading and model selection;

representing a line set without upgradeCombining;

represents a line set upgraded to line 1;

representing a collection of lines upgraded to line type 2,

representing the user waiting time at time t for charging station k;

indicating the maximum waiting time allowed for the user,

、

、

、

respectively representing the upper limit of allowable power of an original line, an upgrade line type 1, an upgrade line type 2 and a newly-built line;

(A23)

in the formula (I), the compound is shown in the specification,

in order to provide the comprehensive satisfaction degree for the user,

in order to satisfy the satisfaction degree of the electricity cost,

in order to be satisfactory in an electrical manner,

、

respectively are the weight values of the satisfaction degree of the electricity cost and the satisfaction degree of the electricity using mode,

and

determines the degree of user's attention to the two satisfaction degrees, and

；

(A24)

(A25)

in the formula, C_DSR、

Respectively represent the electricity costs of the users before and after the execution of DSR,

、

respectively representing the total electric quantity of the load before and after the DSR participation; the constraint conditions of the load layer planning include, in addition to the power balance constraint, the operation safety constraint, the power distribution network connectivity and the radial constraint mentioned in the source layer and the network layer planning, the upper and lower limits constraints of the TOU load transfer-in/out electric quantity balance and the interruptible load shedding proportion, which are respectively as follows:

(A26)

(A27)

(A28)

in the formula, P_s,tTo implement TOU

Quarterly

Electricity consumption of a time period; p_TLO,s、P_TLI，s,tRespectively after the implementation of TOU

Quarterly

The time interval transferring load value and the transferring load value;

respectively after the execution of TOU

Quarterly

The time interval is transferred out of the lower limit and the upper limit of the load proportion;

respectively after the execution of TOU

Quarterly

Shifting the time period to the lower limit and the upper limit of the load proportion;

respectively represent

Quarterly

In the first period

Cutting off upper limit and lower limit of each node load;

(A29)

in the formula (I), the compound is shown in the specification,

representing a curtailment total of the distributed power;

respectively represent

At the first moment

The active power reduction of the platform wind power and photovoltaic equipment; the lower layer constraint conditions comprise DG output reduction constraint and transformer tap adjustment range constraint:

(A30)

(A31)

in the formula (I), the compound is shown in the specification,

indicating the maximum allowable DG reduction, T_kThe position of the tap of the transformer is indicated,

and

respectively representing the lower limit and the upper limit of the regulating range of the tap of the transformer;

the specific contents of implementing information transfer between the source network load three layers and information transfer between the planning layer and the operation simulation layer in step S4 are as follows:

in the upper planning layer, decision contents of source network charge layers are all influenced and restricted mutually, and in each round of circular optimization, the distributed power supply and energy storage optimization result of the source layer obtained in the step S31 under the current topology and load condition is used

Transmitting the information to the other two layers, performing decision of a line and a charging station by combining the information of the source layer and the current load condition through the network layer, transmitting the decision to the load layer, and finally optimizing the result of combining the source layer and the network layer in the load layer according to the step S33, namely optimizing by adopting the optimization model of the load layer in the step S33, and transmitting the electricity utilization condition of the user to the next cycle; adopting a multi-scene technology and an opportunity constraint planning method in the information transmission of the upper layer and the lower layer to enable the upper layer scene which does not meet the constraint to enter the lower layer, adopting active management measures, and transmitting the optimized scene back to the upper layer planning model;

the specific solving process of the coordinated planning model in step S5 is as follows:

step S55: calculating the fitness value, namely calculating the objective function value of the source, net and load layer, namely the formula (A11), (A19), (A23) and (A29); setting the fitness to infinity for scenes which do not meet the constraint condition so as to eliminate the individual in iteration;

(A32)

in the formula (I), the compound is shown in the specification,

and

are inertia weight respectively

The initial value and the final value of (c);

and

、

and

are respectively learning factors

、

The initial value and the final value of (c); m_kAnd M_maxRespectively the current iteration times and the maximum iteration times;

2. The active power distribution network source-network-load-storage coordination planning method considering the electric vehicle charging station as claimed in claim 1, is characterized in that: the step S1 specifically includes the following steps:

(A1)

in the formula (I), the compound is shown in the specification,

、

and

respectively the cut-in wind speed, the rated wind speed and the cut-out wind speed of the WTG;

rated output power of WTG;

(A2)

in the formula (I), the compound is shown in the specification,

is the rated output power of the PVG,

is the rated illumination intensity;

(A3)

in the formula (I), the compound is shown in the specification,

represents the remaining power level of BESS at time t,

represents the loss rate of BESS residual electricity per hour, which is called self-discharge rate for short and has the unit of%/h,

respectively represents the magnitude of BESS charging and discharging power,

、

respectively, the charge and discharge efficiencies of BESS, E_eIs the capacity of the BESS and,

is the sampling interval; by node

Load value P at time_Li（t）And DG output value P_DGi（t）The difference between the two values is used as the equivalent load,

first, an equivalent load is defined

Equivalent load to average

：

(A4)

(A5)

In the formula, P_Li(t)And P_DGi(t)Respectively is a load value and a DG output value of the node i at the moment;

is provided with

For charging power when

Charging the battery; when in use

The storage battery is charged, and in the formula,

the fluctuation coefficient is the fluctuation coefficient around the average value under the equivalent load; is provided with

For the discharge power when

Discharging the battery; when in use

The battery is discharged.

3. The active power distribution network source-network-load-storage coordination planning method considering the electric vehicle charging station as claimed in claim 1, is characterized in that: the specific content of step S2 is:

setting the shortest path of an electric vehicle user as a travel scheme, calculating the travel scheme by using a Floyd shortest path algorithm, and calculating the traffic flow demand intercepted by a quick charging station in the area to be planned each year by using a gravity space interaction model:

(A6)

(A7)

(A8)

in the formula (I), the compound is shown in the specification,

as the shortest path

In a period of time

Per unit value of the one-way traffic flow demand;

and

are respectively paths

Starting point of (2)

And an end point

The weight of (2) to represent the busy degree of each traffic node;

is a path

Per unit value of length;

and

respectively for electric vehicle users in time periods

And the trip proportion in peak time;

the shortest path set is obtained by using a shortest path model, wherein all nodes of the system are connected in pairs;

is a unit

At the fast charging station in the time period

Intercepted traffic flow;

to represent a path

Whether or not to pass through the cell

A binary variable of (d);

is a unit

Whether a binary variable of the quick charging station is constructed or not is judged;

is a traffic network road set;

is a traffic flow economic benefit value;

an economic benefit conversion factor for the intercepted traffic flow;

the average arrival rate of the vehicles to be charged is the proportional allocation of the total frequency demand of the quick charge to the time and the nodes

(A9)

In the formula (I), the compound is shown in the specification,

and

the average arrival rates of the vehicles to be charged at the unit i, namely the reciprocal of the average arrival time interval of the electric automobile users, are respectively the time t of the quick charging station and the traffic peak time;

the total frequency requirement for rapid charging of the system;

(A10)

in the formula (I), the compound is shown in the specification,

is a unit

At the fast charging station in the time period

The charging power of (a);

charging power for a single quick charging device;

the average service rate of a single quick charging device, namely the reciprocal of the average time of quick charging;